
PD and DD (Pitch and Duration Distribution Similarity): We calculate the distribution (frequency histogram) of pitches and durations in melodies, and measure the similarity (average overlapped area [1]}) of the distribution between generated melodies and ground-truth melodies: \(\frac{1}{N_{\text{s}}}\sum _{i=1}^{N_{\text{s}}}OA(\text{Dis}_i,\hat{\text{Dis}}_i)\) , where \(\text{Dis}_i\)  and \(\hat{\text{Dis}}_i\)  represent the pitch or duration distribution of the \(i\) -th generated and ground-truth song respectively, \(N_{s}\)  is the number of songs in the testset, OA represents the average overlapped area.

MD (Melody Distance): To evaluate the pitch trend of the melody, we spread out the notes into a time series of pitch according to the duration, with a granularity of 1/16 note. We subtract each pitch with the average pitch of the entire sequence for normalization. To measure the similarity between the generated and ground-truth time series with different lengths, we use dynamic time warping [2]} to measure their distance.

Alignment Accuracy: To evaluate the alignment between melodies and lyrics, for each token in the source sequence, we calculate how many tokens in the target sequence (generated or ground-truth) are aligned to it, and check if the number of the tokens in the generated sequence equals to that in the ground-truth sequence. We calculate the ratio of equals among all source tokens and all songs in the test set to obtain the alignment accuracy.

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